Transformer Architecture and Attention Questions

Comprehensive understanding of Transformer architecture and attention mechanisms including the principles of self attention where queries keys and values are used to compute attention weights with appropriate scaling. Understand scaled dot product attention and multi head attention and why parallel attention heads improve representational capacity. Know positional encoding schemes including absolute positional encodings relative positional encodings rotary position encodings and alternative methods for injecting order information. Be able to explain encoder and decoder components feed forward networks residual connections and layer normalization and their role in training stability and optimization. Discuss attention variants and efficiency improvements such as sparse attention local windowed attention linear attention kernel based approximations and other methods to reduce memory and compute cost along with their trade offs. At senior and staff levels be prepared to reason about scaling Transformers to very large parameter counts including distributed training strategies parameter and data parallelism memory management and attention pattern design for long sequences and efficient inference. Be ready to apply this knowledge to sequence modeling language modeling and sequence transduction tasks and to justify architectural and implementation trade offs.

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You need to implement rotary positional embeddings (RoPE) in a Transformer training pipeline using PyTorch. Describe the mathematical operation, exactly how you apply RoPE to Q and K tensors (shapes and index mapping), how to implement it efficiently for batched tensors, and how to handle inference when sequences are longer than training sequences.

EasyTechnical

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Compare absolute sinusoidal positional encodings, learned absolute positional embeddings, and relative positional encodings. Explain the advantages and disadvantages of each for language modeling and sequence-to-sequence tasks. Briefly describe rotary positional embeddings (RoPE) and when they are preferable.

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A product manager asks: 'Can we use attention weights to explain why a Transformer made a prediction?' How would you answer? Discuss limitations of using raw attention scores as explanations and propose alternative interpretability techniques suitable for production use.

MediumTechnical

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You need to support real-time inference on documents of 100k tokens with acceptable latency. Propose attention patterns (global tokens, sliding window, hierarchical summarization, chunking and downsampling) to reduce compute while preserving cross-chunk dependencies. Explain how to implement cross-chunk attention and how you would evaluate the quality loss introduced.

EasyTechnical

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Describe the architectural differences and typical use cases for encoder-only, decoder-only, and encoder-decoder Transformer models (for example, BERT, GPT, and T5). Explain how attention masks and input pipelines differ among them, and why one architecture is chosen over another for tasks like classification, language modeling, and translation.

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